There are also several modifications of a Dreamer algorithm that improve its efficiency. For instance, the authors of Dreamer-v2 [1]} offers multiple changes that allow modified algorithm produce a policy that can perform as well as state-of-the-art algorithms do on the Atari Games benchmark. However, most proposed modifications are not fundamental and mostly tunes algorithms to the set of environments. Another important modification that we should mention is a Dreaming algorithm [2]} that replaces observation reconstruction with a contrastive learning algorithm, which makes the world model learn more informative state representations.
